title: Pre-ictal heart rate variability assessment of epileptic seizures by means of linear and non-linear analyses.
abstract: OBJECTIVE
  The purpose of the present study was to analyze the effects of epilepsy on the autonomic control of the heart in pre-ictal phase in order to find an algorithm of early detection of seizure onset.
  
  
  METHODS
  Overall 133 epileptic seizures were analyzed from 12 patients with epilepsy (seven males and five females; mean age 43.91 years, SD: 10.16) participated in this study. Single lead electrocardiogram recordings of epileptic patients were compiled. 240, 90-30, 30-10 and 5 minutes heart rate variability (HRV) signals of preseizure were chosen for analysis of heart rate. As HRV signals are non-stationary, a set of time and frequency domain features (Mean HR, Triangular Index, LF, HF, LF/HF) and nonlinear parameters (SD1, SD2 and SD2/SD1 indices derived from Poincare plots) extracted from HRV is analyzed. Statistical analysis was performed using paired sample t-test for comparisons of the segments and differences between pre-ictal segments were evaluated by Tukey tests.
  
  
  RESULTS
  There was slight tachycardia in segments near the seizure (30 minutes before: 85.3517 bpm, 5 minutes before: 119.3630.82 bpm, p=0.0207) which significantly differ from baseline in segments far from seizure (240 minutes before: 66.5211.7 bpm). Also there was significant increase in LF/HF ratio (30 minutes before: 1.10.22, 5 minutes before: 2.120.5, p=0.0332) and SD2/SD1 ratio (30 minutes before: 1.20.15, 5 minutes before: 2.030.55, p=0.0431) when compared to segments far from the seizure (240 minutes before: 0.780.24 and 0.780.14) respectively. Although there was about decrease of triangular index in segments near the seizure the percentage of decrease was not comparable to segments far from the seizure.
  
  
  CONCLUSIONS
  Significant changes of HRV parameters in pre-ictal (5 minutes before the seizure) are obviously higher in comparison to interictal baseline. Pre-ictal significant changes of HRV suggesting that this time can be considered as prediction time for designing an algorithm of early detection of seizure onset based on HRV.
authors.affiliation: Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran-Iran. sor.behbahani@gmail.com.
authors.forename: Soroor
authors.initials: S
authors.lastname: Behbahani
authors.forename: Nader Jafarnia
authors.initials: NJ
authors.lastname: Dabanloo
authors.forename: Ali Motie
authors.initials: AM
authors.lastname: Nasrabadi
authors.forename: Cesar A
authors.initials: CA
authors.lastname: Teixeira
authors.forename: Antonio
authors.initials: A
authors.lastname: Dourado
mesh_terms: D000328:Adult; D004562:Electrocardiography; D004827:Epilepsy / Q000503:physiopathology*; D005260:Female; D006339:Heart Rate; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D017711:Nonlinear Dynamics; D012640:Seizures / Q000503:physiopathology*; D013610:Tachycardia / Q000503:physiopathology*